DataShop @CMU

Table of Contents

1. What is DataShop?
2. Getting Data In
   1. Logging New Data
   2. Importing New Data
      1. Tab-delimited format
      2. XML format
3. Accessing DataShop
   1. Requesting Dataset Access
4. Project / Dataset Administration
   1. IRB & Data Shareability
   2. Creating new Datasets from existing Samples
5. Citing DataShop and Datasets
6. Glossary
7. Reports
   1. Dataset Info
      1. Overview
      2. Samples
      3. KC Models
      4. Custom Fields
      5. Problem List
      6. Step List
      7. Problem Content
   2. Files
   3. Performance Profiler
   4. Error Report
   5. Learning Curve
      1. Examples
      2. Algorithm
      3. Student-Step Rollup
      4. Model Values
   6. Workflows
8. Filtering Data
   1. By student, KC, and problem
   2. Sample Selector
9. Export: Getting Data Out
   1. Export Format History
10. Using Other Tools
    1. External Tools
    2. R (software)
    3. Excel PivotTables
11. Advanced
    1. Web Services Credentials
       1. User Agreement
    2. Logging Activity
    3. Metrics
12. Examples
13. Contact Us

Sample Selector

Sample Selector is a tool for creating and editing samples, or groups of data you compare across—they're not "samples" in the statistical sense, but more like filters.

By default, a single sample exists: "All Data". With the Sample Selector, you can create new samples to organize your data.

You can use samples to:

• Compare across conditions
• Narrow the scope of data analysis to a specific time range, set of students, problem category, or unit of a curriculum (for example)

A sample is composed of one or more filters, specific conditions that narrow down your sample.

• Creating a sample
• The effect of multiple filters
• Filter Operator Reference (eg, =, <, Like, In)
• Procedures

Creating a sample

The general process for creating a sample is to:

• Add a filter from the categories at the left to the composition area at the right
• Modify the filter to select the subset of data you're interested in, saving it when done
• View the sample preview table to see the effect of adding your filter, making sure you don't have an empty set (ie, a filter or combination of filters that exclude all transactions).
• Name and describe the sample
• Decide whether to share the sample with others who can view the dataset
• Save the sample

The effect of multiple filters

DataShop interprets each filter after the first as an additional restriction on the data that is included in the sample. This is also known as a logical "AND". You can see the results of multiple filters in the sample preview as soon as all filters are "saved".

The columns available to filter on are organized into categories. The categories are:

• Condition
• Dataset Level
• Problem
• School
• Student
• Tutor Transaction

Filter Operator Reference

• = Equals; needs to match exactly to include those rows
• ≠ Not equal; an exact non-match
• < Less than
• > Greater than
• ≤ Less than or equal
• ≥ Greater than or equal
• In Includes rows where the column value is equal to any of several values in a specified list (a logical "OR"). Specify values in the form ('item one', 'item two', 'item three').
• Not In Like "In", but only includes rows whose column value does not match any of the values in the list.
• Like Includes rows whose column value matches a pattern you define. Use the underscore character (_) to match any single character; and the percent sign (%) to match any number of characters (including no characters). Unquoted text is considered to be '%text%'. Single-quoted text will be matched exactly as it is entered.
• Not Like Similar to "Like", but excludes rows with values that match the pattern.
• Is Null Is not set; has no value. This is different than a value being empty ("").
• Is Not Null Is set to something; has a value. This could be any value, including an empty value ("").

Note: Operators ≠, Not In, and Not Like all include rows where the value being compared is null.

To define a new sample:

To define a new sample, click the New Sample button ()to the right of the word Samples.

Enter a name and description for your new sample.

If you check 'Share this Sample', your sample will be viewable to all persons who have access to the current dataset. Only you will be able to modify or delete the sample.

Note: the All Data sample cannot be deleted.

To add a column filter:

1. Select a column category using the combobox underneath the label Column Categories. Selecting a new column category updates the list of available columns directly beneath it.
2. Select the desired column to use as a filter.
3. Press Add to add the filter.

To modify a column filter:

1. Select the column filter from by clicking its row from the list of filtered columns.
2. Select an operator (eg, =, <, or Like).
3. Press Set to update the filter.
4. Repeat steps 1-3 to modify existing filters.
5. Examine the Data Preview below for an idea of what your sample will look like. When you are happy with your sample, press Save to save the sample.
6. Your sample now appears in the list of Samples in the sample list to the left of the Sample Selector.
7. Close the Sample Selector by pressing the X in the upper-right corner.

Note: More than one Column Filter acts as an 'AND' operation. An example would be adding both a student id filter and a problem name filter. This would return transactions that matched both the student and problem filters. To do OR on the same column type (i.e. Student 1 OR Student 2) use the 'In' operator described above.

To delete a column filter:

• Click the delete icon () to the right of the filter in the Filtered Columns section to remove just one filter or select the filter and press Remove.

To modify a sample:

1. Close the sample selector if it is open.
2. Click the edit icon () to the right of the name of the sample that you'd like to modify.

To delete a sample:

1. Close the sample selector if it is open.
2. Click the delete icon () to the right of the sample name that you'd like to remove.